This section explains the file naming convention used for this product with an example. The date and time correspond to the first scan of the granule.

Example file name: AMSR_E_L2_Rain_V09_200705172328_A.hdf

AMSR_E_L2_Rain_X##_yyyymmddhh_f.hdf

Refer to Table 2 for the valid values for the file name variables listed above.

Table 2. Valid Values for the File Name Variables

X

Product Maturity Code (Refer to Table 3 for valid values.)

##

file version number

yyyy

four-digit year

mm

two-digit month

dd

two-digit day

hh

hour, listed in UTC time, of first scan in the file

mm

minute, listed in UTC time, of first scan in the file

f

orbit direction flag (A = ascending, D = descending)

Table 3. Valid Values for the Product Maturity Code

Product Maturity Code

Description

P

Preliminary - refers to non-standard, near-real-time data available from NSIDC. These data are only available for a limited time until the corresponding standard product is ingested at NSIDC.

B

Beta - indicates a developing algorithm with updates anticipated.

T

Transitional - period between beta and validated where the product is past the beta stage, but not quite ready for validation. This is where the algorithm matures and stabilizes.

V

Validated - products are upgraded to Validated once the algorithm is verified by the algorithm team and validated by the validation teams. Validated products have an associated validation stage. Refer to Table 4 for a description of the stages.

Table 4. Validation Stages

Validation Stage

Description

Stage 1

Product accuracy is estimated using a small number of independent measurements obtained from selected locations, time periods, and ground-truth/field program efforts.

Stage 2

Product accuracy is assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts.

Stage 3

Product accuracy is assessed, and the uncertainties in the product are well-established via independent measurements made in a systematic and statistically robust way that represents global conditions.

Table 5 provides examples of file name extensions for related files that further describe or supplement data files.

Temporal coverage is from 18 June 2002 to 3 October 2011. Each swath spans approximately 50 minutes. See the AMSR-E Data Versions Web page for a summary of temporal coverage for different AMSR-E products and algorithms.

Each swath spans approximately 50 minutes. The data sampling interval is 2.6 msec per sample for the 6.9 GHz to 36.5 GHz channels, and 1.3 msec for the 89.0 GHz channel. A full scan takes approximately 1.5 seconds. AMSR-E collects 243 data points per scan for the 6.9 GHz to 36.5 GHz channels, and 486 data points for the 89.0 GHz channel.

Satellite-based estimates of rain rate and rain type rely primarily on modeling the absorption and emission effects on microwave signals for specified cloud temperatures, water vapor, and hydro meteor profiles. Atmospheric transmittance windows below 20 GHz, from 30 GHz to 40 GHz, and at 90 GHz are used for rainfall monitoring. Below 20 GHz, rainfall absorption and emission are predominant, and ocean surfaces are warmer than the background radiation. Above 60 GHz, evidence of rainfall is primarily from scattering, where areas of heavy rainfall are colder than their backgrounds. Between 20-60 GHz, a combination of absorption and scattering is present.

A radiative transfer equation that includes absorption and scattering coefficients is the basis for deriving rain rate from brightness temperatures in this data set. The absorption and scattering coefficients, which are summarized in more detail in (Kummerow and Ferraro 2007 ), are expressed as an integral over the range of rain drop sizes. Radiative transfer calculations are used to determine brightness temperatures given atmospheric temperature, water vapor, and hydro meteor profiles. These computations are carried out for the AMSR-E frequencies of 6.9, 10.7, 18.7, 36.6 and 89.0 GHz and 54 degree incidence angle, and for different freezing levels.

At all channels, brightness temperatures increase toward a maximum and then drop off as rainfall rates increase further. The main difference between channels is the range of rainfall rates for which the curve increases in the emission region and decreases in the scattering region (Kummerow and Ferraro 2007 ). The brightness temperature at low frequencies is primarily a function of absorption. The rain rate follows from the absorption coefficient implied by the measurements. Ice and snow are efficient scatterers of microwave radiation compared with rain. Since land background has a high emissivity, rainfall rate over land must be inferred from the ice-scattering signature, instead of relying on the emission signal from rain drops.

The AMSR-E Level 2 rainfall algorithm in rooted in a Bayesian retrieval scheme over oceans and a regression of scattering signals to surface rainfall over land (Wilheit, Kummerow, and Ferraro 2003) . The ocean retrieval relies primarily on the emission signal from the rain drops themselves while the land retrieval relies solely on the scattering of high frequency (89 GHz) radiation from precipitation sized-ice particles at and above the freezing level. The two approaches are needed due to the vastly different surface emissivities and the resulting differences in the sensor information content over ocean and land, respectively. In order to build a consistent algorithm framework the land portion of the algorithm was converted from its original regression form (Grody 1991) and updated by (Ferraro 1997) to a Bayesian framework, but in such a fashion as to reproduce the original regression equations.

Both the land and ocean algorithms begin with a set of Cloud Resolving Model simulations that prescribe the surface rainfall and the associated hydro meteor profiles. Both schemes use texture information to classify observed scenes as either convective, stratiform or mixed. The Bayesian scheme is then invoked to match the observed brightness temperature with entries in the database that match the observed brightness temperature. The difference between ocean and land thus consists chiefly of two aspects. Over land, the possible profiles are narrowed significantly to only those that match the historically derived brightness temperature to rainfall relations, and over land, more empirical relations are needed in order to discriminate raining scenes from brightness temperature depressions caused by radio metrically cold surfaces. The algorithm is discussed in detail in Kummerow and Ferraro (2007) and Wilheit, Kummerow, and Ferraro (2003).

Instantaneous Ocean Rainfall

The algorithm over oceans uses a representative set of pre computed Cloud Resolving Model (CRM) profiles to establish the relationship between cloud micro physical parameters and up welling brightness temperatures. Once the database of representative profiles and Brightness Temperatures (Tb) are generated, the algorithm uses a Bayesian inversion methodology in the following manner:

Pr(R|Tb) = Pr(R) * Pr(Tb | R)

Where:

Pr(R) = probability that a profile R will be observed
Pr(Tb | R) = probability of observing the set of brightness temperatures given a rain profile R.

The retrieval algorithm thus generates a new cloud profile from the weighted sum of structures in the cloud resolving model data base that are consistent with the observed brightness temperature as well as brightness temperature horizontal variability. The latter is used to determine the convective/stratiform nature of the precipitation. The algorithm is discussed in detail in Kummerow and Ferraro (2007) and Wilheit, Kummerow, and Ferraro (2003)

Instantaneous Land Rainfall

Accurate rainfall retrievals over land are far more difficult than oceanic retrievals due to the large and variable emissivity of the land surface. Specifically, the high emissivity masks the emission signature that is related directly to the water content in the atmosphere. Instead, only the brightness temperature depression due to scattering in the upper portion of clouds is observable. The scattering increases with increasing frequencies. Consequently, brightness temperature depressions at the 89-GHz channel of AMSR-E contain the least ambiguous signal of scattering by ice and/or large raindrops.

A further complication that arises over land is the lack of consistent backgrounds against which to compare the brightness temperature depression. To alleviate the problem caused by the varying emissivity associated with changes in surface characteristics such as surface wetness, snow cover, vegetation, etc., a rain/no-rain temperature depression threshold is required to screen out false identification of rain. Additionally, snow and desert surfaces cause depressed brightness temperatures at high frequencies due to surface volume scattering, and can be confused with the rain signature. If these surface types are not properly screened, they can be misinterpreted as ice scattering in clouds.

The AMSR-E precipitation team decided to use the same GPROF retrieval methodology as used for the ocean retrieval (Wilheit, Kummerow, and Ferraro 2003). However, unlike the ocean component, the initial database of possible profiles was carefully selected to include only those profiles that fit the empirical relation developed by Ferraro and Marks (1995). Thirty-six profiles, out of the several thousand profiles in the GPROF database, were found to satisfy this relationship (McCollum et al. 1999). The team then computed the expected AMSR-E brightness temperatures for these profiles for use in the a-priori look-up table used in the Bayesian inversion algorithm shown above.

Quantifying errors in this data set is complicated, because it involves understanding the nature of precipitation. Uncertainties arise when the rain layer thickness is not well understood, or when inhomogeneous rainfall occurs below the resolution of the satellite. Another potential source of error is the non-precipitating component of clouds, which contribute to brightness temperatures. Scattering-based retrievals over land also present many uncertainties, most notably the lack of a consistent relationship between frozen rain aloft and liquid at lower altitudes. Quantifying the scattering by ice is especially problematic. Ambiguities occur in the data because microwave radiation is scattered not only by rainfall and associated ice, but by snow cover and dry land (Kummerow and Ferraro 2007 ).

A know error exists related to sun glint that results in missing Rain Rate values, and presents as gray ovals in the AE_Rain browse images. Sun glint is not a problem over land; however, the algorithm is using only geometry to determine sun glint causing missing values to exist over land. Sun glint is included in the algorithm because it affects the brightness temperatures out to the missing radius; however, a thorough investigation is yet to be completed. Preliminary investigations indicate that the bias could be up to 15 percent in the affected areas.

Each HDF-EOS file contains core metadata with Quality Assessment (QA) metadata flags that are set by the Science Investigator-led Processing System (SIPS) at the Global Hydrology and Climate Center (GHCC) prior to delivery to NSIDC. A separate metadata file with a .xml file extension is also delivered to NSIDC with the HDF-EOS file, and it contains the same information as the core metadata. Three levels of QA are conducted with the AMSR-E Level 2 and 3 products: automatic, operational, and science. If a product does not fail QA, it is ready to be used for higher-level processing, browse generation, active science QA, archive, and distribution. If a granule fails QA, SIPS does not send the granule to NSIDC until it is reprocessed. Level-3 products that fail QA are never delivered to NSIDC (Conway 2002).

Automatic QA

Brightness temperatures are verified to be within the physical bounds (50 K  305 K) for all channels used by the rainfall algorithm. Automated QA for the rainfall algorithm is difficult because heavy rainfall can mask the surface thereby hindering geo-location verification. The rainfall algorithm therefore relies on the Level 2A product for QA of the geo-location. As a final QA check on the computed rainfall, the brightness temperatures of the computed rainfall are compared to the observed brightness temperatures. If the difference between computed and observed brightness temperatures exceeds a pre-defined threshold, the rainfall is set to missing.

Operational QA

AMSR-E Level-2A data arriving at GHCC are subject to operational QA prior to processing higher-level products. Operational QA varies by product, but it typically checks for the following criteria in a given file (Conway 2002):

File is correctly named and sized

File contains all expected elements

File is in the expected format

Required EOS fields of Time, Latitude, and Longitude are present and populated

Structural metadata are correct and complete

The file is not a duplicate

The HDF-EOS version number is provided in the global attributes

The correct number of input files were available and processed

Science QA

AMSR-E Level-2A data arriving at GHCC are also subject to science QA prior to processing higher-level products. If less than 50 percent of a granule's data are good, the science Q/A flag is marked
suspect when the granule is delivered to NSIDC. In the SIPS environment, the science QA includes checking the maximum and minimum variable values, and percent of missing data and out-of-bounds data per variable value. At the Science Computing Facility (SCF), also at GHCC, science QA involves reviewing the operational QA files, generating browse images, and performing the following additional automated QA procedures (Conway 2002):

Historical data comparisons

Detection of errors in geo-location

Verification of calibration data

Trends in calibration data

Detection of large scatter among data points that should be consistent

Geo-location errors are corrected during Level-2A processing to prevent processing anomalies such as extended execution times and large percentages of out-of-bounds data in the products derived from Level-2A data.

The Team Lead SIPS (TLSIPS) developed tools for use at SIPS and SCF for inspecting the data granules. These tools generate a QA browse image in Portable Network Graphics (PNG) format and a QA summary report in text format for each data granule. Each browse file shows Level-2A and Level-2B data. These are forwarded from the Remote Sensing Systems (RSS) to the GHCC along with associated granule information, where they are converted to HDF raster images prior to delivery to NSIDC. The QA summary reports are available on the GHCC AMSR-E Web page.

Please refer to AMSR-E Validation Data for information about data used to check the accuracy and precision of AMSR-E observations.